Inferring Visual Perceptual Object by Adaptive Fusion of Image Salient Features

Xin Xu, Nan Mu and Hong Zhang

Mathematical Problems in Engineering, 2015, vol. 2015, 1-9

Abstract:

Saliency computational model with active environment perception can be useful for many applications including image retrieval, object recognition, and image segmentation. Previous work on bottom-up saliency computation typically relies on hand-crafted low-level image features. However, the adaptation of saliency computational model towards different kinds of scenes remains a challenge. For a low-level image feature, it can contribute greatly on some images but may be detrimental for saliency computation on other images. In this work, a novel data driven approach is proposed to adaptively select proper features for different kinds of images. This method exploits low-level features containing the most distinguishable salient information per image. Then the image saliency can be calculated based on the adaptive weight selection scheme. A large number of experiments are conducted on the MSRA database to compare the performance of the proposed method with the state-of-the-art saliency computational models.

Date: 2015
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Persistent link: https://EconPapers.repec.org/RePEc:hin:jnlmpe:973241

DOI: 10.1155/2015/973241

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